Built an automated scoring engine to streamline ML hackathon evaluations.
This hackathon engine provides an end-to-end solution for organizing and evaluating machine learning competitions, automating the often complex and time-consuming assessment process.
The system handles submission processing, evaluation against multiple metrics, real-time leaderboard updates, and detailed feedback generation for participants.
Built with scalability in mind, the platform can support concurrent evaluation of hundreds of submissions while maintaining consistent performance.
Implemented a sandboxed evaluation environment using Docker containers with resource limitations and network isolation.
Designed an auto-scaling architecture on Kubernetes that dynamically adjusts resources based on submission queue length.
Created a normalized scoring system that accounts for execution environment differences, ensuring consistent and fair evaluation.
"The ML Hackathon Engine transformed our event organization. What used to take a team of judges days now happens automatically in minutes with consistent and fair evaluations."
Lisa Park
Chief Organizer, Global AI Hackathon
